In the social public security area, a video surveillance system becomes an important part in maintaining public security and strengthening social management. Although the surveillance system has been widely applied in public places such as a bank, a shopping mall, a station, and an intersection, an actual surveillance task still requires relatively much manual support. In addition, an existing video surveillance system usually only records a video. When information is provided, a video image on which extraction has not been performed can only be used for post-event evidence collection, and the real-time quality and initiative of surveillance are not fully exploited. In view of characteristics of a surveillance video such as a large volume of stored data and long storage time, a conventional method for finding a lead and acquiring evidence from a video needs to consume a lot of manpower, material resources, and time, and efficiency is extremely low. Therefore, in the video surveillance system, a playing time of a video event is shortened by means of video extraction, and a retrieval object may be located through fast browsing by means of target classification screening, thereby greatly improving surveillance efficiency.
Currently, there are two manners for performing extraction on a video file. Specific technical solutions are as follows.
A first solution is reading a video file from a local disk, and then performing moving-object detection on the read video file frame by frame using a frame difference method, to obtain a rectangular outline of a moving object, performing precise tracking on the rectangular outline of the detected moving object by means of rectangular outline matching, using an entire motion process of the tracked moving object as a video abstract, and recording detailed information about each video abstract, and finally, after recording of the entire video abstract is completed, extracting each recorded video abstract from the source video file, and combining all video abstracts into a new video, to obtain a video file including all the video abstracts.
A second solution is performing moving-target detection and analysis processing on an original image sequence, to obtain visual feature information of a moving target in each frame of an original video, performing target tracking combination processing between frames; extracting index information of moving targets in the original video, performing time and space resequencing on moving target objects, generating an abstract video by fusing the moving targets and a background image, recording the index information of the moving targets in the abstract video, and establishing an index association between a moving target in each frame of the abstract video and the moving target in the original video. The object tracking combining method in this solution is simple and effective, and implements fast and accurate extraction of an abstract of a video, and an original video clip in which a moving target shows up can be browsed any time using index information.
However, the foregoing two methods for extracting an image from a video use single resolution to perform image extraction, and therefore an extraction speed is decreased and precision and stability of image extraction are reduced.